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"abstract": "Choosing the best cloud configuration for large-scale data analytics jobs deployed in the cloud can substantially improve their performance and reduce costs. However, current cloud providers offer a wide variety of instance types and customized cluster sizes, making it both time-consuming and costly to pinpoint the optimal cloud configuration. This article presents the design, implementation, and evaluation of Silhouette, a cloud configuration selection framework based on performance models for various large-scale analytics jobs with minimal training overhead. The essence of Silhouette is to build performance prediction models with carefully selected small-scale experiments on small subsets of input data to estimate the performance with entire input data on larger cluster sizes. To reduce the training time and cost, Silhouette incorporates new statistical techniques to select those experiments that yield the best possible information for performance prediction. Moreover, we develop a novel model transformer to convert a prediction model built on one instance type to a different instance type with only one extra experiment, which significantly reduces the training overhead. We evaluate Silhouette with an extensive array of large-scale data analytics jobs on Amazon EC2. Our experimental results have shown convincing evidence that Silhouette is effective in optimizing cloud configuration while saving both training time and costs compared with existing solutions.",
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"abstract": "Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between <i>l</i><sub>2,1</sub>ℓ2,1-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data.",
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"content": "Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between <i>l</i><sub>2,1</sub>ℓ2,1-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data.",
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"normalizedAbstract": "Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between l2,1ℓ2,1-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data.",
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"abstract": "The execution time of a stage is extended by a few slow running tasks in Spark computing environments. To tackle this so-called straggler problem, Spark adopts speculative execution mechanism under which the scheduler speculatively launch additional backup for the straggler with the hope to complete early. However, due to the characteristics of tasks and the complexity of runtime environments, the Spark original speculative execution strategy and its improved versions cannot deal with this problem effectively. In this paper, we propose a novel strategy called ETWR to improve the efficiency of speculative execution in Spark. We consider the heterogeneous environment when we around to tackle the three key points of speculative execution: straggler identification, backup node selection and effectiveness guarantee. Based on the task type classification, first, we divide the task into sub-phases and use both the process speed and progress rate within a phase to find the straggler promptly. Second, we use the Locally Weighted Regression model to estimate the execution time of the task, which will be used to calculate the task's remaining time and backup time. Third, we present iMCP model to guarantee the effectiveness of speculative tasks, which can additionally keep load balancing for nodes. Finally, the factors of fast node and better location are considered when choosing proper backup nodes. Extensive experiments show that ETWR can reduce the job execution time by 23.8 percent, and improve the cluster throughput by 33.2 percent compared with Spark-2.2.0.",
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"content": "Mirror detection is challenging because the visual appearances of mirrors change depending on those of their surroundings. As existing mirror detection methods are mainly based on extracting contextual contrast and relational similarity between mirror and non-mirror regions, they may fail to identify a mirror region if these assumptions are violated. Inspired by a recent study of applying a CNN to help distinguish whether an image is flipped or not based on the visual chirality property, in this paper, we rethink this image-level visual chirality property and reformulate it as a learnable pixel level cue for mirror detection. Specifically, we first propose a novel flipping-convolution-flipping (FCF) transformation to model visual chirality as learnable commutative residual. We then propose a novel visual chirality embedding (VCE) module to exploit this commutative residual in multi-scale feature maps, to embed the visual chirality features into our mirror detection model. Besides, we also propose a visual chirality-guided edge detection (CED) module to integrate the visual chirality features with contextual features for detection refinement. Extensive experiments show that the proposed method outperforms state-of-the-art methods on three benchmark datasets.",
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"abstract": "Spatial interpolation is one of the demanding techniques in Geographic Information Science (GISci) to generate interpolated maps in a continuous manner by using two discrete spatial and/or temporal data sets. Noise-free data (thematic layers) depicting a specific theme at varied spatial or temporal resolutions consist of connected components either in aggregated or in disaggregated forms. This short paper provides a simple framework: 1) to categorize the connected components of layered sets of two different time instants through their spatial relationships and the Hausdorff distances between the companion-connected components and 2) to generate sequential maps (interpolations) between the discrete thematic maps. Development of the median set, using Hausdorff erosion and dilation distances to interpolate between temporal frames, is demonstrated on lake geometries mapped at two different times and also on the bubonic plague epidemic spread data available for 11 consecutive years. We documented the significantly fair quality of the median sets generated for epidemic data between alternative years by visually comparing the interpolated maps with actual maps. They can be used to visualize (animate) the spatiotemporal behavior of a specific theme in a continuous sequence.",
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"normalizedAbstract": "Spatial interpolation is one of the demanding techniques in Geographic Information Science (GISci) to generate interpolated maps in a continuous manner by using two discrete spatial and/or temporal data sets. Noise-free data (thematic layers) depicting a specific theme at varied spatial or temporal resolutions consist of connected components either in aggregated or in disaggregated forms. This short paper provides a simple framework: 1) to categorize the connected components of layered sets of two different time instants through their spatial relationships and the Hausdorff distances between the companion-connected components and 2) to generate sequential maps (interpolations) between the discrete thematic maps. Development of the median set, using Hausdorff erosion and dilation distances to interpolate between temporal frames, is demonstrated on lake geometries mapped at two different times and also on the bubonic plague epidemic spread data available for 11 consecutive years. We documented the significantly fair quality of the median sets generated for epidemic data between alternative years by visually comparing the interpolated maps with actual maps. They can be used to visualize (animate) the spatiotemporal behavior of a specific theme in a continuous sequence.",
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"abstract": "Experiential learning (ExL) is the process of learning through experience or more specifically “learning through reflection on doing”. In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support “adaptive guidance”, in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigatō (Augmented Reality Instructional Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task.",
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"title": "Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments",
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"abstract": "Stylizing a 3D model with characteristic shapes or appearances is common in product design, particularly in the design of 3D model merchandise, such as souvenirs, toys, furniture, and stylized items. A model stylization approach is proposed in this study. The approach combines base and style models while preserving user-specified shape features of the base model and the attractive features of the style model with limited assistance from a user. The two models are first combined at the topological level. A tree-growing technique is utilized to search for all possible combinations of the two models. Second, the models are combined at textural and geometric levels by employing a morphing technique. Results show that the proposed approach generates various appealing models and allows users to control the diversity of the output models and adjust the blending degree between the base and style models. The results of this work are also experimentally compared with those of a recent work through a user study. The comparison indicates that our results are more appealing, feature-preserving, and reasonable than those of the compared previous study. The proposed system allows product designers to easily explore design possibilities and assists novice users in creating their own stylized models.",
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